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Detecting the Emotions and Sentiments of Environmental, Social & Governance (ESG)Tweets

This blog is part of a series of analytic exercises that explores the themes related to ESG tweets. To fully understand this blog, please check the previous… ....

A Network Analysis , Tweets Using , Environmental Social , Governance Themes , Co Occurrence Network , Reaking News ,

"Visualization and Analysis of Air Pollution and Human Health Based on " by Diyi Liu, Kun Cheng et al.

Bibliometric techniques and social network analysis are employed in this study to evaluate 14,955 papers on air pollution and health that were published from 2001 to 2021. To track the research hotspots, the principle of machine learning is applied in this study to divide 10,212 records of keywords into 96 clusters through OmniViz software. Our findings highlight strong research interests and the practical need to control air pollution to improve human health, as evidenced by an annual growth rate of over 15.8% in the related publications. The cluster analysis showed that clusters C22 (exposure, model, mortality) and C8 (health, environment, risk) are the most popular topics in this field of research. Furthermore, we develop co-occurrence networks based on the cluster analysis results in which a more specific keyword classification was obtained. These key areas include: “Air pollutant source”, “Exposure-Response relationship”, “Public & Occupational Health”, and so on. ....

Public Occupational Health , Air Pollution , Co Occurrence Network , Machine Learning , Social Network Analysis ,